Arthroscopic Repair of Anterior Labroligamentous Periosteal Sleeve Avulsion Lesions Does Not Have an Increased Failure Rate Compared to Arthroscopic Bankart Repair
Bibliographic record
Abstract
Objectives: Anterior labroligamentous periosteal sleeve avulsion lesions (ALPSA) have been identified as a potential risk factor for failure of an arthroscopic labral repair. The objective of this study was to compare the failure rates and clinical outcomes of arthroscopic ALPSA repair to arthroscopic Bankart repair. Additionally, the role of glenoid bone loss on failure rates was analyzed within each group. Methods: This was a retrospective review of 72 consecutive patients with anterior shoulder instability (73 shoulders) who underwent an anterior arthroscopic labral repair at a single military institution by one of three Sports medicine fellowship trained orthopaedic surgeons. At the time of surgery, a diagnostic arthroscopy identified 13 (17.8%) ALPSA lesions and 60 (82.2%) isolated Bankart lesions. All lesions were repaired and placed on standard post-operative protocol. Data was collected on demographics, the Western Ontario Shoulder Instability (WOSI) score, SANE score, and recurrence rates. Failure was defined as recurrent dislocation. Additionally, glenoid bone loss in all patients was calculated using a standardized technique on preoperative images. Outcomes were analyzed by type of initial lesion. The effect of bone loss on failure rate was analyzed between and within groups. Results: The average age at surgery was 26.3 years (range, 20-42) with an average follow-up of 53.3 months (range, 28-63). There were 13 distinct ALPSA lesions and 60 Bankart lesions identified on diagnostic arthroscopy. There were no significant differences between groups with respect to any demographic data. There was 1 failure (7.7%) in the ALSPA group and 8 failures (13.3%) in the Bankart group (p=0.10). There was no significant difference between groups for WOSI or SANE scores. There was no significant difference in glenoid bone loss between groups. The ALPSA group had 13.1% glenoid bone loss compared to 13.5% in the Bankart group (p=0.88). Conclusion: Contrary to previously published data, we did not find patients with ALPSA lesions to be at an increased risk for failure of an arthroscopic repair compared to an isolated Bankart repair, nor was there a difference in functional outcomes between groups. Finally, the presence of an ALPSA lesion was not predictive on increased glenoid bone loss in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".